A general theory of polynomial conjoint measurement,
dc.contributor.author | Tversky, Amos | en_US |
dc.date.accessioned | 2006-04-17T16:12:39Z | |
dc.date.available | 2006-04-17T16:12:39Z | |
dc.date.issued | 1967-02 | en_US |
dc.identifier.citation | Tversky, Amos (1967/02)."A general theory of polynomial conjoint measurement,." Journal of Mathematical Psychology 4(1): 1-20. <http://hdl.handle.net/2027.42/33362> | en_US |
dc.identifier.uri | http://www.sciencedirect.com/science/article/B6WK3-4DTKCW8-Y/2/d72afc466de22fe4ef48147ebf9a3899 | en_US |
dc.identifier.uri | https://hdl.handle.net/2027.42/33362 | |
dc.description.abstract | The present theory generalizes conjoint measurement in five major respects. (a) It is formulated in terms of partially rather than fully ordered data. (b) It applies to both ordinal and numerical data. (c) It is applicable to finite as well as infinite data structures. (d) It provides a necessary and sufficient condition for measurement. (e) This condition applies to any polynomial measurement model; that is, any model where each data element is expressed as a specified real-valued, order-preserving polynomial function of its components.Examples of polynomial measurement models include Savage's subjective expected utility model, Hull's and Spence's performance models, Luce's choice model, and multidimensional scaling models.It is shown that a data structure D satisfies a given polynomial measurement M if and only if D satisfies an abstract irreflexivity axiom with respect to M. The interpretation of the result and its implications to measurement theory are discussed. | en_US |
dc.format.extent | 1222022 bytes | |
dc.format.extent | 3118 bytes | |
dc.format.mimetype | application/pdf | |
dc.format.mimetype | text/plain | |
dc.language.iso | en_US | |
dc.publisher | Elsevier | en_US |
dc.title | A general theory of polynomial conjoint measurement, | en_US |
dc.type | Article | en_US |
dc.rights.robots | IndexNoFollow | en_US |
dc.subject.hlbsecondlevel | Psychology | en_US |
dc.subject.hlbtoplevel | Social Sciences | en_US |
dc.description.peerreviewed | Peer Reviewed | en_US |
dc.contributor.affiliationum | The University of Michigan, Ann Arbor, Michigan, USA | en_US |
dc.description.bitstreamurl | http://deepblue.lib.umich.edu/bitstream/2027.42/33362/1/0000760.pdf | en_US |
dc.identifier.doi | http://dx.doi.org/10.1016/0022-2496(67)90039-9 | en_US |
dc.identifier.source | Journal of Mathematical Psychology | en_US |
dc.owningcollname | Interdisciplinary and Peer-Reviewed |
Files in this item
Remediation of Harmful Language
The University of Michigan Library aims to describe library materials in a way that respects the people and communities who create, use, and are represented in our collections. Report harmful or offensive language in catalog records, finding aids, or elsewhere in our collections anonymously through our metadata feedback form. More information at Remediation of Harmful Language.
Accessibility
If you are unable to use this file in its current format, please select the Contact Us link and we can modify it to make it more accessible to you.